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| Exploring and Anticipating Early Detection Methods for Scientific and Technological Weak Signals |
| Zhang Huiling1, Xu Haiyun2, Liu Chunjiang3, Chen Liang4, Wang Chao2, Wang Haiyan4 |
1.Taiyuan City Public Library, Taiyuan 030024
2.Business School, Shandong University of Technology, Zibo 255000
3.National Science Library (Chengdu), Chinese Academy of Sciences, Chengdu 610299
4.Institute of Scientific and Technical Information of China, Beijing 100038 |
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Abstract Weak signals in science and technology (WSST) are indicators of future technological advancements, offering insights into innovation trends, emerging opportunities, and market directions. However, there is currently a lack of comprehensive method libraries for early WSST perception. Existing approaches are limited in their ability to mine weak technological signal features and suffer from low interpretability and subjective constraints. This article systematically categorizes potential models and methods for WSST perception, with the goal of enabling intelligent analysis for technology foresight. We begin by examining the essence of WSST and the attributes of intelligent perception. Then, we integrate three methodological dimensions—quantitative and qualitative, subjective and objective, and causal and correlative—to compile a method library and toolset for early WSST perception. By assessing WSST characteristics, applicability, and interpretability, we identify methods suitable for various stages of signal perception. Additionally, we evaluate intelligent perception techniques, highlighting their advantages and limitations, while proposing future research directions. Case studies in the field of stem cells validate the feasibility of the proposed perception methods and processes. Moving forward, there is a need for novel cognitive perception models and traceability methods for WSST, aimed at expanding characteristics coverage and developing perception techniques driven by digital intelligence to support emerging technologies in uncertain environments.
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Received: 11 March 2024
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